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About Microsoft R Client

02/16/2018

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Microsoft R Client is a free, community-supported, data science tool for high performance analytics. R Client is built on top of Microsoft R Open so you can use any open-source R package to build your analytics. Additionally, R Client includes the powerful RevoScaleR technology and its proprietary functions to benefit from parallelization and remote computing.

R Client allows you to work with production data locally using the full set of RevoScaleR functions, but there are some constraints. Data must fit in local memory, and processing is limited to two threads for RevoScaleR functions. To work with larger data sets or offload heavy processing, you can access a remote production instance of Machine Learning Server from the command line or push the compute context to the remote server. Learn more about its compatibility.

Machine Learning Server vs R Client

Machine Learning Server and Microsoft R Client offer virtually identical R packages, but each one targets different scenarios. R Client is intended for data scientists who create solutions that run locally. Machine Learning Server is commercial software that runs on a range of platforms, at much greater scale, with infrastructure for handling major workloads, on client-server topologies that support remote access over authenticated connections.

You can work with R Client standalone. You can also use it with Machine Learning Server, where you learn and develop on R Client, and then migrate your work to Machine Learning Server or execute it remotely on an Machine Learning Server whenever you need the scale, support, and infrastructure of a server configured for operationalization.

From R Client, shift data-centric RevoScaleR operations to a remote Machine Learning Server by creating a remote compute context. Remote compute context is supported for SQL Server Machine Learning Services or a Spark cluster. Typically, you shift the compute context to bring computations to where the data resides, thus avoiding data transfer over the network.

From R Client, run arbitrary R code on a remote production instance of Machine Learning Server. This is a general-purpose capability: from a command line, you can switch between local and remote sessions interactively, useful for testing, administration, or to use the additional processing power of a production server. For remote code execution, use mrsdeployand remoteLogin() or remoteLoginAAD(). For more information, see Execute on a remote server .

Get started with R Client

Getting started with Microsoft R Client is as easy as 1-2-3. Click a step to get started:

![Step 1](./media/
what-is-microsoft-r-client/Step1.png)

1. Install R Client

The first step is to download Microsoft R Client for your operating system and install it. To learn more about the supported platforms or installation steps, please see the following articles:

2. Configure Your IDE

While R is a command line driven program, you can also use your favorite R integrated development environment (IDE) to interact with Microsoft R Client. To do so, you must point that IDE to the R Client R executable. This way, whenever you execute your R code, you'll do so using R Client and benefit from the proprietary packages installed with R Client. R IDE options include R Tools for Visual Studio on Windows (Recommended), RStudio, or any other R development environment.

Set up RTVS for R Client on Windows: R Tools for Visual Studio (RTVS) is an integrated development environment available as a free add-in for any edition of Visual Studio. To make R Client the default R engine for RTVS, choose Change R to Microsoft R Client from the R Tools menu.

Set up RStudio for R Client on Windows or Linux: RStudio is another popular R IDE. To make R Client the default R engine for RStudio, update the path to R. For example, point to C:\Program Files\Microsoft\R Client\R_SERVER\bin\x64 on Windows.

After you configure the IDE, a message appears in the console signaling that the Microsoft R Client packages were loaded.

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